The 5 Essential Components of a Data Strategy Title

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Thitlee5 Essential Components of a Data Strategy

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Contents

Data Strategy: What Problem Does It Solve?.................................................. 1 Data: Past and Present......................................................................................... 2 The Business Without a Data Strategy............................................................. 2 Data Strategy Defined......................................................................................... 4 The 5 Components of a Data Strategy ............................................................ 4

Identify .............................................................................................................................5 Store.................................................................................................................................6 Provision ..........................................................................................................................8 Process.............................................................................................................................9 Govern .........................................................................................................................10 Defining a Data Strategy Is Key .......................................................................12 The Power of a Data Strategy...........................................................................12 Learn More...........................................................................................................13

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Despite heavy, long-term investments in data management, data problems at many organizations continue to grow. One reason is that data has traditionally been perceived as just one aspect of a technology project; it has not been treated as a corporate asset. Consequently, the belief was that traditional application and database planning efforts were sufficient to address ongoing data issues.

As our corporate data stores have grown in both size and subject area diversity, it has become clear that a strategy to address data is necessary. Yet some still struggle with the idea that corporate data needs a comprehensive strategy.

There's no shortage of blue-sky thinking when it comes to organizations' strategic plans and road maps. To many, such efforts are just a novelty. Indeed, organizations' strategic plans often generate very few tangible results for organizations ? only lots of meetings and documentation. A successful plan, on the other hand, will identify realistic goals along with a road map that provides clear guidance on how to best get the job done.

Let's see how this played out in real life at one organization that set out to develop a data strategy.

Data Strategy: What Problem Does It Solve?

Consider the example of a consulting team helping a large bank to develop a data strategy. From the start, the project champion had found it hard to get his VP to understand the need for and importance of a data strategy. Why?

The bank was already successful. Its revenue and costs were well-managed, and the individual business units and technology groups were good at delivering against their commitments. To the bank's credit, it wasn't complacent. Management was always looking for ways to increase staff members' productivity and reduce ongoing costs. There were all kinds of metrics and key performance indicators (KPIs) to measure IT performance, business benefits and total cost of ownership. The idea of building yet another road map to address a problem that wasn't well-understood met with pushback.

The VP gave his explanation along with some questions:

"We've got dozens of projects going on at any given time. We're very good at managing our storage needs, our application systems, the analytical platforms, software costs and individual project budgets. Every project identifies staff and resource costs, and we don't ever move forward without the business covering the costs.

Why do we need a data strategy? What problem will it solve?"

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With the bank doing so many things right, he needed to understand why and how a data strategy would make a difference. To answer these questions, it's important to consider how data was created and used in the past compared to how it's created and used today.

Data: Past and Present

Once upon a time, data was perceived as a byproduct of a business activity or process. It had little value after the process was completed. While there might have been one or two other applications that needed to access the content for follow-up (e.g., customer service, special reports, audits, etc.), these were usually one-off activities.

Today, business is very different. The value of data is accepted; the results of reporting and analytics have made data the secret sauce of many new business initiatives. It's common for application data to be shared with as many as 10 other systems.

While the value of data has evolved tremendously over the past 20 years ? and business users recognize it ? few companies have adjusted their approaches to capturing, sharing and managing corporate data assets. Their behavior reflects an outdated, underlying belief that data is simply an application byproduct.

Organizations need to create data strategies that match today's realities. To build such a comprehensive data strategy, they need to account for current business and technology commitments while also addressing new goals and objectives.

The Business Without a Data Strategy

Thinking back to the story, the bank executive's concerns were not hard to understand. He spent lots of time wading through project proposals that his devoted staff was incredibly emotional about. In many instances, his team's project proposals were about delivering perfection ? turning something that already worked into something faster, stronger or better. The executive understood the world of finite budgets and resources where any new approved project would ultimately take funding and resources away from another request. His mantra was well-known:

"Tell me why your idea is more important than the items already on the priority list."

The consultants were prepared for this discussion.

The issue was not related to the premise or value of any individual project. The problem was the approach that each individual project and activity took. Each activity addressed data needs independently from one another without any awareness of the overlapping efforts and costs. ? Most projects required access to the same data content. Unfortunately, there was no

coordination to prevent overlapping (and wasted) work. ? There was no data sharing, no data reuse, or any economies-of-scale activities to

simplify or reduce the cost of data movement and development.

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? Business users accessed common data across separate applications. Data value names and formatting varied across applications.

? Users found inconsistencies across reports because source data wasn't documented, and it varied across individual reports.

The result was duplicate data, processing overlaps and little awareness that individual projects were replicating work. There wasn't anything in place to support communicating, collaborating or sharing data methods and practices across projects and systems.

The problem: Every project at the bank addressed data issues as one-off, built-fromscratch activities.

Case Study: The Bank's Data Challenges The bank's IT team had 17 projects underway (new applications, application enhancements, new reports, etc.).

? Each project required access to customer data, and each had overlapping tasks and resources.

? Every project included a source data inventory and analysis activity because there was no way to know where specific data resided.

? New data extracts (subsets of the application's data copied for use by other systems) had to be built because IT had no way of determining if the data was already available.

? No two teams shared their source extract data. Each had their own copies to support their integration and database build activities (which tied up storage for this transient content).

? Each team's integration logic was custom built and individually maintained, because the logic and rules weren't identified or documented to be shared.

The business staff ? dependent on its own operational and reporting efforts ? had experienced other challenges:

? Marketing had to continually update its campaign system to adjust to frequent (and uncommunicated) changes occurring to the layouts of the extracts it received.

? Sales managers always had questions about KPI reports with customer details because titles and labels varied across reports (even though they contained common data).

? Business unit users often built their own reports instead of using the standard reports from finance, because there was no way to determine the origin of standard report data.

? The data warehousing team had to continually chase data problems because data issues weren't managed like other business support activities.

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Data Strategy Defined

The concepts of standards, collaboration and reuse are well-understood across organizations within most companies. Most development teams are well-educated about system architecture, development methods, requirements gathering, testing and even code reusability. Most business teams can recite the concepts of business requirements, business process definition and results measurement. Unfortunately, the notion of applying these concepts to data to support improved accuracy, access, sharing and reuse is still foreign to most organizations.

The idea behind developing a data strategy is to make sure all data resources are positioned in such a way that they can be used, shared and moved easily and efficiently. Data is no longer a byproduct of business processing ? it's a critical asset that enables processing and decision making. A data strategy helps by ensuring that data is managed and used like an asset. It provides a common set of goals and objectives across projects to ensure data is used both effectively and efficiently. A data strategy establishes common methods, practices and processes to manage, manipulate and share data across the enterprise in a repeatable manner.

While most companies have multiple data management initiatives underway (metadata, master data management, data governance, data migration, modernization, data integration, data quality, etc.), most efforts are focused on point solutions that address specific project or organizational needs. A data strategy establishes a road map for aligning these activities across each data management discipline in such a way that they complement and build on one another to deliver greater benefits.

The 5 Components of a Data Strategy

Historically, IT organizations have defined data strategy with a focus on storage. They've built comprehensive plans for sizing and managing their platforms and they've developed sophisticated methods for handling data retention. While this is certainly important, it actually addresses the tactical aspects of content storage ? it's not planning for how to improve all of the ways you acquire, store, manage, share and use data.

A data strategy must address data storage, but it must also take into account the way data is identified, accessed, shared, understood and used. To be successful, a data strategy has to include each of the different disciplines within data management. Only then will it address all of the issues related to making data accessible and usable so that it can support today's multitude of processing and decision-making activities.

There are five core components of a data strategy that work together as building blocks to comprehensively support data management across an organization: identify, store, provision, process and govern.

A data strategy is a plan designed to improve all of the ways you acquire, store, manage, share and use data.

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Identify

Govern

The Core

Store

Components

Process

Provision

Figure 1: The five core components of a data strategy.

Identify

Identify data and understand its meaning regardless of structure, origin or location

One of the most basic constructs for using and sharing data within a company is establishing a means to identify and represent the content. Whether it's structured or unstructured content, manipulating and processing data isn't feasible unless the data value has a name, a defined format and value representation (even unstructured data has these details). Establishing consistent data element naming and value conventions is core to using and sharing data. These details should be independent of how the data is stored (in a database, file, etc.) or the physical system where it resides.

It's also important to have a means of referencing and accessing metadata associated with your data (definition, origin, location, domain values, etc.). In much the same way that having an accurate card catalog supports an individual's success in using a library to retrieve a book, successful data usage depends on the existence of metadata (to help retrieve specific data elements). Consolidating business terminology and meaning into a business data glossary is a common means to addressing part of the challenge.

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Libraries have card catalogs because it's impractical to remember the location of every book. Metadata is critical for business data usage because it's impossible to know the location and meaning of all of the company's business data ? thousands of data elements across numerous data sources. Without data identification details, you would be forced to undertake a data inventory and analysis effort every time you wanted to include new data in your processing or analysis activities.

Without a data glossary and metadata (i.e., the "data card catalog"), companies are likely to ignore some of their most prized data assets because they won't know they exist. If data is truly a corporate asset, a data strategy has to ensure that all of the data can be identified.

Location

Product

Customer

Attribute Customer ID First Name Last Name Middle Initial Home Street Home City ... ...

Source SalesCRM CapBilling CapBilling CapBilling ServCont ServCont ... ...

De nition Value uniquely identifying Customer's rst name Customer's last name Customer's middle initial Home street address Home residence city ... ...

Type

... ... Steward

Integer

... ... Susan Craff

Character ... ... Susan Craff

Character ... ... Susan Craff

Character ... ... Susan Craff

Character ... ... Susan Craff Character ... ... Susan Craff

...

... ... ...

...

... ... ...

Figure 2: A data card catalog.

Store

Persist data in a structure and location that supports easy, shared access and processing

Data storage is one of the basic capabilities in a company's technology portfolio ? yet it is a complex discipline. Most IT organizations have mature methods for identifying and managing the storage needs of individual application systems; each system receives sufficient storage to support its own processing and storage requirements. Whether dealing with transactional processing applications, analytical systems or even general purpose data storage (files, email, pictures, etc.), most organizations use sophisticated methods to plan capacity and allocate storage to the various systems. Unfortunately, this approach only reflects a "data creation" perspective. It does not encompass data sharing and usage.

The gap in this approach is that there's rarely a plan for efficiently managing the storage required to share and move data between systems. The reason is simple; the most visible data sharing in the IT world is transactional in nature. Transactional details between applications are moved and shared to complete a specific business process. Bulk data sharing isn't well-understood and is often perceived as a one-off or infrequent occurrence.

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